Take-home Exercise 4: Prototyping Modules for Visual Analytics Shiny Application

1. Project Overview

Our group project aims to solve the problem of the Singapore rental market by creating a visual application based on R Shiny. The core objective of the project is to accurately predict the future trend of housing rental prices in various regions in order to provide tenants with informed rental decision support.

In Take-home Exercise 4, our task was to prototype the visual application that our team had designed and select the appropriate Shiny UI components. This step is designed to ensure that our team project can be completed successfully, showcasing the functionality and features of the design through suitable Shiny UI components that make it easy and intuitive for users to interact with the application.

Through this process, we verify that the selected R package is supported, test the correctness of the R code, and determine the parameters and output exposed in the Shiny application. Our goal is to create a full-featured, user-friendly visualization tool to help users gain insight into rental trends in different parts of Singapore, thereby enhancing tenants’ decision-making skills.

2. Data Preparation

Our group obtained data on rental rates in Singapore from the Urban Redevelopment Authority. In order to obtain the required information, we conducted a search on the website and selected the data that met our needs. We then consolidate and merge this data and save it into a CSV file.

2.1 Install and Load packages

pacman::p_load(ggplot2, dplyr, tidyr, plotly,corrplot,readr,ggstatsplot,png)

2.2 Import Data

Rental_data <- read_csv("data/ResidentialRental_Final.csv")

head(Rental_data)

# A tibble: 6 × 21
  Column1  Year Project_Name   Street_Name       Postal_District Planning_Region
    <dbl> <dbl> <chr>          <chr>                       <dbl> <chr>          
1       0  2021 1 LOFT         LORONG 24 GEYLANG              14 East Region    
2       0  2021 1 CANBERRA     CANBERRA DRIVE                 27 North Region   
3       0  2021 HILLVIEW PARK  HILLVIEW AVENUE                23 West Region    
4       0  2021 STRATA         ESSEX ROAD                     11 Central Region 
5       0  2021 EASTERN LAGOON UPPER EAST COAST…              15 East Region    
6       0  2021 ONE JERVOIS    JERVOIS CLOSE                  10 Central Region 
# ℹ 15 more variables: Property_Type <chr>, No_of_Bedroom <dbl>,
#   Monthly_Rent_SGD <dbl>, Monthly_Rent_PSM <dbl>, Monthly_Rent_PSF <dbl>,
#   Floor_Area_SQM_Avg <dbl>, Floor_Area_SQFT_Avg <dbl>,
#   Lease_Commencement_Date <chr>, interest_rate <dbl>, nearest_mrt <chr>,
#   distance_to_mrt <dbl>, nearest_school <chr>, distance_to_school <dbl>,
#   latitude <dbl>, longitude <dbl>

3. Analysis

3.1 Exploratory Data Analysis (EDA)

Analysis of rental influencing factors: Exploratory data analysis is performed by exploring the relationship between Monthly_Rent_SGD and Floor_Area_SQFT_Avg.And I set Property_Type and Planning_Region as faceted variables.

3.1.1 Correlation between Floor_Area_SQM_Avg and Monthly_Rent_SGD

ggscatterstats(data = Rental_data,
               x = Floor_Area_SQM_Avg, y = Monthly_Rent_SGD,
               type = "nonparametric") +
  facet_wrap(vars(!!sym("Property_Type"))) +
  labs(x = "Floor_Area_SQM_Avg", y = "Monthly_Rent_SGD") +
  theme_minimal()

ggscatterstats(data = Rental_data,
               x = Floor_Area_SQM_Avg, y = Monthly_Rent_SGD,
               type = "nonparametric") +
  facet_wrap(vars(!!sym("Planning_Region"))) +
  labs(x = "Floor_Area_SQM_Avg", y = "Monthly_Rent_SGD") +
  theme_minimal()

Insights: Looking at the correlation between Floor_Area_SQM_Avg and Monthly_Rent_SGD, we found that regardless of the area or type of home, the general trend was that the larger the average size of the home, the higher the monthly rent.

3.1.2 Explore the relationship between No_of_Bedroom and Floor_Area_SQM_Avg

boxplot <- plot_ly(data = Rental_data, 
                   x = ~No_of_Bedroom, 
                   y = ~Floor_Area_SQM_Avg, 
                   type = "box", 
                   boxpoints = FALSE,  # Outliers are not displayed
                   jitter = 0.3,
                   line = list(color = "black"))  

median_values <- Rental_data %>%
  group_by(No_of_Bedroom) %>%
  summarise(median_value = median(Floor_Area_SQM_Avg, na.rm = TRUE))

boxplot <- boxplot %>% 
  add_lines(x = median_values$No_of_Bedroom, 
            y = median_values$median_value, 
            mode = "lines",
            line = list(color = "red"),
            name = "Median Line")

layout <- list(
  xaxis = list(title = "No_of_Bedroom"),
  yaxis = list(title = "Floor_Area_SQM_Avg")
)

interactive_boxplot <- boxplot %>% layout(layout)

interactive_boxplot

Insights: From the trend of this box plot, we can see that usually the more rooms, the larger the area of the house.

3.2 Confirmatory Data Analysis (CDA)

3.2.1 Verification 1 - Housing type significantly affects monthly rent.

H0: There is no significant difference in monthly rent by housing type.

H1: Monthly rent varies significantly depending on the type of house.

# In the linear model,Monthly_Rent_SGD is the response variable and Property_Type is the predictor variable
lm_model <- lm(Monthly_Rent_SGD ~ Property_Type, data = Rental_data)

# Perform ANOVA tests
anova_result <- anova(lm_model)

print(anova_result)

Analysis of Variance Table

Response: Monthly_Rent_SGD
                  Df     Sum Sq    Mean Sq F value    Pr(>F)    
Property_Type      4 4.0278e+11 1.0069e+11   11981 < 2.2e-16 ***
Residuals     192194 1.6153e+12 8.4048e+06                      
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

if (anova_result$`Pr(>F)`[1] < 0.05) {
  cat("Rejecting the null hypothesis, there is a significant difference.\n")
} else {
  cat("Failing to reject the null hypothesis, monthly rents do not differ significantly between different housing types.\n")
}

Rejecting the null hypothesis, there is a significant difference.

Insights: The conclusion “reject the null hypothesis, there is a significant difference” means that there is sufficient evidence that different housing types have a significant effect on monthly rents.

A visualization plot showing the distribution of monthly rent across different housing types

plot_ly(data = Rental_data, 
        x = ~Property_Type, 
        y = ~Monthly_Rent_SGD, 
        type = "violin", 
        box = list(visible = TRUE),
        points = "none",  
        color = ~Property_Type) %>%
  layout(xaxis = list(title = "Property Type"),
         yaxis = list(title = "Monthly Rent SGD"))

3.2.2 Verification 2 - Planning area has a significant impact on monthly rent

H0: There is no significant difference in monthly rents by planning area.

H1: Monthly rent varies significantly depending on the planned area.

# In the linear model, Monthly_Rent_SGD is the response variable and Planning_Region is the predictor variable
lm_model_region <- lm(Monthly_Rent_SGD ~ Planning_Region, data = Rental_data)

anova_result_region <- anova(lm_model_region)

print(anova_result_region)

Analysis of Variance Table

Response: Monthly_Rent_SGD
                    Df     Sum Sq    Mean Sq F value    Pr(>F)    
Planning_Region      4 1.9013e+11 4.7534e+10  4997.7 < 2.2e-16 ***
Residuals       192194 1.8280e+12 9.5112e+06                      
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

if (anova_result_region$`Pr(>F)`[1] < 0.05) {
  cat("Rejecting the null hypothesis, there is a significant difference in monthly rents between different planning regions.\n")
} else {
  cat("Failing to reject the null hypothesis, monthly rents do not differ significantly between different planning regions.\n")
}

Rejecting the null hypothesis, there is a significant difference in monthly rents between different planning regions.

Insights: We reject the null hypothesis that monthly rents vary significantly across planning areas.

A visualization plot showing the distribution of monthly rent across different planning region

grouped_bar_chart <- ggplot(Rental_data, aes(x = Planning_Region, y = Monthly_Rent_SGD, fill = Planning_Region)) +
  geom_bar(stat = "summary", fun = "mean") +
  labs(x = "Planning Region", y = "Mean Monthly Rent SGD") +
  theme_minimal()

interactive_grouped_bar_chart <- ggplotly(grouped_bar_chart)

interactive_grouped_bar_chart

3.2.3 Verification 3 - The number of bedrooms has a significant effect on the size of the house

H0: The number of bedrooms has no significant effect on the size of the house.

H1: The number of bedrooms has a significant effect on the size of the house.

# In the linear model, Floor_Area_SQM_Avg is the response variable and No_of_Bedroom is the predictor variable
lm_model <- lm(Floor_Area_SQM_Avg ~ No_of_Bedroom, data = Rental_data)

anova_result <- anova(lm_model)

print(anova_result)

Analysis of Variance Table

Response: Floor_Area_SQM_Avg
                  Df    Sum Sq   Mean Sq F value    Pr(>F)    
No_of_Bedroom      1 288590572 288590572  221623 < 2.2e-16 ***
Residuals     164673 214431948      1302                      
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

if (anova_result$`Pr(>F)`[1] < 0.05) {
  cat("Rejecting the null hypothesis, there is a significant difference.\n")
} else {
  cat("Failing to reject the null hypothesis, bedroom count does not significantly affect floor area.\n")
}

Rejecting the null hypothesis, there is a significant difference.

Insights: We reject the null hypothesis, which means that the number of bedrooms has a significant effect on the size of the house, or that there is a significant linear relationship between the number of bedrooms and the size of the house.

A visualization plot showing the distribution of the number of bedrooms and house area

filtered_data <- Rental_data %>% na.omit(c("No_of_Bedroom", "Floor_Area_SQM_Avg", "Property_Type"))

lm_model <- lm(Floor_Area_SQM_Avg ~ No_of_Bedroom, data = filtered_data)

predictions <- predict(lm_model, filtered_data)

plot_ly(data = filtered_data, 
        x = ~No_of_Bedroom, 
        y = ~Floor_Area_SQM_Avg, 
        type = "scatter",
        mode = "markers",
        marker = list(color = ~Property_Type),
        text = ~paste("Property Type: ", Property_Type, "<br>No of Bedroom: ", No_of_Bedroom, "<br>Floor Area SQM Avg: ", Floor_Area_SQM_Avg)) %>%
  add_lines(x = ~No_of_Bedroom, 
            y = ~predictions, 
            type = "scatter", 
            mode = "lines", 
            line = list(color = "red")) %>%
  layout(xaxis = list(title = "No of Bedroom"),
         yaxis = list(title = "Floor Area SQM Avg"))

4. UI Design

I learned that the UI Design Storyboard should include three parts: Summary, Data exploration analysis and Forecasting.

library(shiny)
library(shinydashboard)
library(DT)
library(shinyjs)
library(plotly)
library(ggplot2)

Rental_data <- read.csv("data/ResidentialRental_Final.csv")

# Define UI
ui <- dashboardPage(
  dashboardHeader(title = "Rental Data Analysis"),
  dashboardSidebar(
    sidebarMenu(
      menuItem("Summary", tabName = "summary"),
      menuItem("Data exploration analysis", tabName = "data_exploration"),
      menuItem("Forecasting", tabName = "forecasting")
    )
  ),
  dashboardBody(
    tabItems(
      # Summary tab
      tabItem(tabName = "summary",
              fluidRow(
                box(title = "Summary", status = "info", solidHeader = TRUE,
                    textOutput("summary_text"))
              ),
              fluidRow(
                box(title = "Top 10 Rows of the Dataset", status = "primary", solidHeader = TRUE,
                    DTOutput("top_rows_table"))
              )
      ),
      
      # Data exploration analysis tab
      tabItem(tabName = "data_exploration",
              fluidRow(
                box(title = "EDA: Correlation Plot", status = "primary", solidHeader = TRUE,
                    plotOutput("correlation_plot"))
              ),
              fluidRow(
                box(title = "EDA: Box Plot", status = "warning", solidHeader = TRUE,
                    plotlyOutput("box_plot"))
              ),
              fluidRow(
                box(title = "CDA: ANOVA Results", status = "success", solidHeader = TRUE,
                    verbatimTextOutput("anova_results"))
              ),
              fluidRow(
                box(title = "CDA: Violin Plot", status = "danger", solidHeader = TRUE,
                    plotlyOutput("violin_plot"))
              ),
              fluidRow(
                box(title = "CDA: Grouped Bar Chart", status = "info", solidHeader = TRUE,
                    plotlyOutput("grouped_bar_chart"))
              )
      ),
      
      # Forecasting tab
      tabItem(tabName = "forecasting",
              fluidRow(
                box(title = "Forecasting Analysis", status = "info", solidHeader = TRUE,
                    textOutput("forecasting_text"))
              )
      )
    )
  )
)

# Define server
server <- function(input, output) {
  output$summary_text <- renderText({
    "Housing rent-related data description."
  })

  # Render top 10 rows of the dataset
  output$top_rows_table <- renderDT({
    datatable(head(Rental_data, 10))
  })

  # Render correlation plot
  output$correlation_plot <- renderPlot({
    ggplot(Rental_data, aes(x = Floor_Area_SQM_Avg, y = Monthly_Rent_SGD)) +
      geom_point() +
      labs(x = "Floor_Area_SQM_Avg", y = "Monthly_Rent_SGD") +
      theme_minimal()
  })

  # Render box plot
  output$box_plot <- renderPlotly({
    boxplot <- plot_ly(data = Rental_data,
                       x = ~No_of_Bedroom,
                       y = ~Floor_Area_SQM_Avg,
                       type = "box",
                       boxpoints = FALSE,
                       jitter = 0.3,
                       line = list(color = "black"))

    median_values <- Rental_data %>%
      group_by(No_of_Bedroom) %>%
      summarise(median_value = median(Floor_Area_SQM_Avg, na.rm = TRUE))

    boxplot <- boxplot %>%
      add_lines(x = median_values$No_of_Bedroom,
                y = median_values$median_value,
                mode = "lines",
                line = list(color = "red"),
                name = "Median Line")

    boxplot
  })

  # Render ANOVA results
  output$anova_results <- renderPrint({
    lm_model <- lm(Monthly_Rent_SGD ~ Property_Type + Planning_Region, data = Rental_data)
    anova_result <- anova(lm_model)
    print(anova_result)
    
    if (anova_result$`Pr(>F)`[1] < 0.05) {
      cat("Rejecting the null hypothesis, there is a significant difference.\n")
    } else {
      cat("Failing to reject the null hypothesis, monthly rents do not differ significantly between different housing types.\n")
    }
  })

  # Render violin plot
  output$violin_plot <- renderPlotly({
    plot_ly(data = Rental_data,
            x = ~Property_Type,
            y = ~Monthly_Rent_SGD,
            type = "violin",
            box = list(visible = TRUE),
            points = "none",
            color = ~Property_Type) %>%
      layout(xaxis = list(title = "Property Type"),
             yaxis = list(title = "Monthly Rent SGD"))
  })

  # Render grouped bar chart
  output$grouped_bar_chart <- renderPlotly({
    grouped_bar_chart <- ggplot(Rental_data, aes(x = Planning_Region, y = Monthly_Rent_SGD, fill = Planning_Region)) +
      geom_bar(stat = "summary", fun = "mean") +
      labs(x = "Planning Region", y = "Mean Monthly Rent SGD") +
      theme_minimal()

    ggplotly(grouped_bar_chart)
  })

  # Render forecasting text
  output$forecasting_text <- renderText({
    "Based on the current analysis, we believe that, irrespective of the region, larger houses tend to have higher rents. In other words, the more rooms a house has, the higher its monthly rental price."
  })
}

# Run the application
shinyApp(ui, server)

Shiny applications not supported in static R Markdown documents

Static R Markdown documents do not support shiny applications, so I took a screenshot of the UI design part I did to show it.

Summary

Data exploration analysis

Forecasting

5. Learning Point

In the process of learning UI design, I found that it is a new knowledge field, and there are still many places for me to deeply understand and study hard. UI design is critical in designing visual analytics applications, so I thought that mastering this skill would positively impact my data science and analytics work.